Connectome-Based Predictive Modeling of Individual Anxiety

Zhihao Wang, Katharina S. Goerlich, Hui Ai, Andre Aleman, Yue-Jia Luo, Pengfei Xu

Research output: Contribution to journalArticleAcademicpeer-review

34 Citations (Scopus)
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Abstract

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational "lesion" approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.

Original languageEnglish
Pages (from-to)3006-3020
Number of pages15
JournalCerebral Cortex
Volume31
Issue number6
Early online date29-Jan-2021
DOIs
Publication statusPublished - Jun-2021

Keywords

  • anxiety
  • computational lesion
  • connectome-based predictive modeling (CPM)
  • limbic system
  • resting-state functional connectivity
  • STATE FUNCTIONAL CONNECTIVITY
  • GENERALIZED ANXIETY
  • EMOTION REGULATION
  • REVERSE INFERENCE
  • BRAIN ACTIVATION
  • MOTION ARTIFACT
  • DEPRESSION
  • FMRI
  • AMYGDALA
  • NETWORK

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